Learning to Reason: Leveraging Neural Networks for Approximate DNF Counting

Weighted model counting (WMC) has emerged as a prevalent approach for probabilistic inference. In its most general form, WMC is #P-hard. Weighted DNF counting (weighted #DNF) is a special case, where approximations with probabilistic guarantees are obtained in O(nm), where n denotes the number of variables, and m the number of clauses of the input DNF, but this is not scalable in practice. In this paper, we propose a neural model counting approach for weighted #DNF that combines approximate model counting with deep learning, and accurately approximates model counts in linear time when width is bounded. We conduct experiments to validate our method, and show that our model learns and generalizes very well to large-scale #DNF instances.Comment: To appear in Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). Code and data available at: https://github.com/ralphabb/NeuralDNF

The Surprising Power of Graph Neural Networks with Random Node Initialization

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. This universality result holds even with partially randomized initial node features, and preserves the invariance properties of GNNs in expectation. We then empirically analyze the effect of RNI on GNNs, based on carefully constructed datasets. Our empirical findings support the superior performance of GNNs with RNI over standard GNNs.Comment: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21). Code and data available at http://www.github.com/ralphabb/GNN-RN

BoxE: A Box Embedding Model for Knowledge Base Completion

Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB). A promising approach for KBC is to embed knowledge into latent spaces and make predictions from learned embeddings. However, existing embedding models are subject to at least one of the following limitations: (1) theoretical inexpressivity, (2) lack of support for prominent inference patterns (e.g., hierarchies), (3) lack of support for KBC over higher-arity relations, and (4) lack of support for incorporating logical rules. Here, we propose a spatio-translational embedding model, called BoxE, that simultaneously addresses all these limitations. BoxE embeds entities as points, and relations as a set of hyper-rectangles (or boxes), which spatially characterize basic logical properties. This seemingly simple abstraction yields a fully expressive model offering a natural encoding for many desired logical properties. BoxE can both capture and inject rules from rich classes of rule languages, going well beyond individual inference patterns. By design, BoxE naturally applies to higher-arity KBs. We conduct a detailed experimental analysis, and show that BoxE achieves state-of-the-art performance, both on benchmark knowledge graphs and on more general KBs, and we empirically show the power of integrating logical rules.Comment: Proceedings of the Thirty-Fourth Annual Conference on Advances in Neural Information Processing Systems (NeurIPS 2020). Code and data available at: http://www.github.com/ralphabb/Box

PlanE: Representation Learning over Planar Graphs

Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism invariant on graphs, which makes the learned representations graph invariants. On the other hand, it is well-known that graph invariants learned by these class of models are incomplete: there are pairs of non-isomorphic graphs which cannot be distinguished by standard graph neural networks. This is unsurprising given the computational difficulty of graph isomorphism testing on general graphs, but the situation begs to differ for special graph classes, for which efficient graph isomorphism testing algorithms are known, such as planar graphs. The goal of this work is to design architectures for efficiently learning complete invariants of planar graphs. Inspired by the classical planar graph isomorphism algorithm of Hopcroft and Tarjan, we propose PlanE as a framework for planar representation learning. PlanE includes architectures which can learn complete invariants over planar graphs while remaining practically scalable. We empirically validate the strong performance of the resulting model architectures on well-known planar graph benchmarks, achieving multiple state-of-the-art results.Comment: Proceedings of the Thirty-Seventh Annual Conference on Advances in Neural Information Processing Systems (NeurIPS 2023). Code and data available at: https://github.com/ZZYSonny/Plan

The fallacy of the BUN:creatinine ratio in critically ill patients

Abstract Background and objectives. Acute kidney injury (AKI) is common in critically ill patients and is associated with a high mortality rate. Pre-renal azotemia, suggested by a high blood urea nitrogen to serum creatinine (BUN:Cr) ratio (BCR), has traditionally been associated with a better prognosis than other forms of AKI. Whether this pertains to critically ill patients is unknown. Methods. We conducted a retrospective observational study of two cohorts of critically ill patients admitted to a single center: a derivation cohort, in which AKI was diagnosed, and a larger validation cohort. We analyzed associations between BCR and clinical outcomes: mortality and renal replacement therapy (RRT). Results. Patients in the derivation cohort (N ¼ 1010) with BCR >20 were older, predominantly female and white, and more severely ill. A BCR >20 was significantly associated with increased mortality and a lower likelihood of RRT in all patients, patients with AKI and patients at risk for AKI. Patients in the validation cohort (N ¼ 10 228) with a BCR >20 were older, predominantly female and white, and more severely ill. A BCR >20 was associated with increased mortality and a lower likelihood of RRT in all patients and in those at risk for AKI, BUN correlated with age and severity of illness. Conclusions. A BCR >20 is associated with increased mortality in critically ill patients. It is also associated with a lower likelihood of RRT, perhaps because of misinterpretation of the BCR. Clinicians should not use a BCR >20 to classify AKI in critically ill patients

Tako-tsubo cardiomyopathy after administration of ergometrine following elective caesarean delivery: a case report

<p>Abstract</p> <p>Introduction</p> <p>Tako-tsubo cardiomyopathy (stress-induced cardiomyopathy or transient left ventricular ballooning) is characterized by clinical suspicion of an acute myocardial infarction with transient apical or midventricular dyskinesia of the left ventricle without significant coronary stenosis on angiography. The etiology of this disease remains obscure. One of the possible causes is myocardial ischemia induced by coronary vasospasm due to sympathetic activation. It has been hypothesized that the application of ergometrine could induce tako-tsubo cardiomyopathy.</p> <p>Case presentation</p> <p>We report the case of a 28-year-old Turkish woman who developed tako-tsubo cardiomyopathy after administration of ergometrine for release of placenta and prevention of bleeding during the post-partum phase in the course of an elective caesarean delivery. Tako-tsubo cardiomyopathy was diagnosed by echocardiography and urgent cardiac magnetic resonance imaging. A coronary angiography was not performed because of the absence of myocardial necrosis or ischemia and signs of myocarditis on cardiac magnetic resonance imaging.</p> <p>Conclusion</p> <p>This life-threatening disease should be excluded in the differential diagnosis by comparing the symptoms with those of typical heart failure, particularly after use of ergometrine.</p

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes